Snowflake Certified SnowPro Specialty - Snowpark : SPS-C01

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame named 'employee_df with columns 'employee_id', 'department', and 'salary'. You want to calculate the average salary for each department and add it as a new column named 'avg_department_salary' to the original DataFrame. Additionally, you want to sort the resulting DataFrame by department and then by salary in descending order. Which of the following Snowpark code snippets correctly implements this requirement?

A)

B)

C)

D)

E)

2. You are working with sensor data in Snowpark. Your data contains (Integer), 'timestamp' (Timestamp), 'temperature' (Double), and 'status' (String). You need to create a Snowpark DataFrame named representing this data'. Which of the following is the most efficient and type-safe way to create the DataFrame from a list of Python tuples using an explicitly defined schema, assuming you need to maintain maximum precision for temperature readings and that all data types should map to the most efficient and appropriate Snowflake data type?

A)

B)

C)

D)

E)

3. Consider the following Snowpark code snippet that aims to calculate the rank of each employee based on their salary within their respective department. What are potential issues with this code, and how can you improve it? (Select all that apply.)

A) There may be performance issues if the employee table is very large. Consider adding a filter to the DataFrame before applying the window function.
B) The 'rank()' function will produce dense ranks, which might be undesirable if there are ties in salary. Use for contiguous ranks instead.
C) The code is correct and will produce the desired output without any issues.
D) The code does not handle potential null values in the salary column. Consider using or before calculating the rank.
E) It is missing the 'col' function call in the orderBy clause. It should be 'orderBy(sf.col("salary").desc())'.

4. You have written a Snowpark Python function that utilizes a UDF to perform complex string manipulation on a DataFrame containing customer reviews. When deploying this function using '@sproc.test_utils.mock_snowflake environment, the test fails with a 'ModuleNotFoundError' indicating that a custom Python library (e.g., is not available. You have already confirmed that the library is installed in your local development environment. What is the MOST reliable way to ensure the UDF has access to this dependency during local testing?

A) Manually copy the directory into the 'ltmp' directory on your local machine before running the test. Snowpark will automatically detect libraries in this directory.
B) Install the globally on your local machine using 'pip install
C) Append the path to the library to the 'PYTHONPATH' environment variable before executing the local tests. This will make the library available to the Snowpark session during testing.
D) Include the directory within the same directory as your Snowpark Python function and explicitly import it using a relative path (e.g., 'from .text_processing_lib import ...4).
E) Use before defining the UDF to explicitly include the library as a zipped file. Ensure the zipped file contains the directory.

5. You have a Snowpark DataFrame 'df with a column 'transaction_date' of STRING type, containing dates in 'YYYY-MM-DD' format, and a 'transaction_amount' column of STRING type, containing currency values like '$1 ,234.56'. You need to create a new DataFrame 'df_transformed' that contains the 'transaction_date' as a DATE type and 'transaction_amount' as a DOUBLE type. What Snowpark Python code snippet will accomplish this transformation most efficiently and handle potential errors during the cast?

A) ...python df_transformed = df.with_column('transaction_date', to_date(col('transaction_date'))).with_column('transaction_amount', to_double(col('transaction_amount').replace('$',").replace(',',")))
B) ...python from snowflake.snowpark.functions import try_to_date, try_to_double df_transformed = df.with_column('transaction_date', try_to_date(col('transaction_date'))).with_column('transaction_amount', try_to_double(regexp_replace(col('transaction_amount'), '[$,]', ")))
C) ...python df_transformed = df.select(to_date(col('transaction_date')).alias('transaction_date'), to_double(col('transaction_amount')).alias('transaction_amount'))
D) ...python df_transformed = df.with_column('transaction_date', to_date(col('transaction_date') YYYY-MM-DD')).with_column('transaction_amount', col('transaction_amount').cast(DoubleType()))
E) ...python import snowflake.snowpark.functions as sf df_transformed = df.with_column('transaction_date', sf.to_date(sf.col('transaction_date'), format='YYYY- MM-DD')).with_column('transaction_amount', sf.to_double(sf.regexp_replace(sf.col('transaction_amount'), '[$,]', ")

Solutions: